Kernel Creation from MATLAB Code

MATLAB code structures and patterns that create CUDA® GPU kernels

GPU Coder™ generates and executes optimized CUDA kernels for specific algorithm structures and patterns in your MATLAB® code. The generated code calls optimized NVIDIA® CUDA libraries, including cuFFT, cuSolver, cuBLAS, cuDNN, and TensorRT. The generated code can be integrated into your project as source code, static libraries, or dynamic libraries, and can be compiled for desktops, servers, and GPUs embedded on NVIDIA Jetson, DRIVE, and other platforms. GPU Coder lets you incorporate handwritten CUDA code into your algorithms and into the generated code.

Apps

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GPU CoderGenerate GPU code from MATLAB code
GPU Environment CheckVerify and set up GPU code generation environment

Functions

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codegenGenerate C/C++ code from MATLAB code
gpucoderOpen GPU Coder app
coder.checkGpuInstallVerify GPU code generation environment
coder.gpuConfigConfiguration parameters for CUDA code generation from MATLAB code by using GPU Coder
halfConstruct half-precision numeric object
coder.gpu.kernelPragma that maps for-loops to GPU kernels
coder.gpu.kernelfunPragma that maps function to GPU kernels
coder.gpu.nokernelPragma to disable kernel creation for loops
coder.gpu.constantMemoryPragma that maps a variable to the constant memory on GPU
gpucoder.stencilKernelCreate CUDA code for stencil functions
gpucoder.matrixMatrixKernelOptimized GPU implementation of functions containing matrix-matrix operations
gpucoder.batchedMatrixMultiplyOptimized GPU implementation of batched matrix multiply operation
gpucoder.stridedMatrixMultiplyOptimized GPU implementation of strided and batched matrix multiply operation
gpucoder.batchedMatrixMultiplyAddOptimized GPU implementation of batched matrix multiply with add operation
gpucoder.stridedMatrixMultiplyAddOptimized GPU implementation of strided, batched matrix multiply with add operation
coder.gpu.persistentMemoryPragma to allocate a variable as persistent memory on the GPU
gpucoder.sortOptimized GPU implementation of the MATLAB sort function
coder.gpu.iterationsPragma that provides information to the code generator for making parallelization decisions on variable bound loops
gpucoder.transposeOptimized GPU implementation of the MATLAB transpose function
gpucoder.reduceOptimized GPU implementation for reduction operations
coder.cevalCall external C/C++ function

Objects

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coder.gpuConfigConfiguration parameters for CUDA code generation from MATLAB code by using GPU Coder
coder.CodeConfigConfiguration parameters for C/C++ code generation from MATLAB code
coder.EmbeddedCodeConfigConfiguration parameters for C/C++ code generation from MATLAB code with Embedded Coder
coder.gpuEnvConfigCreate configuration object containing the parameters passed to coder.checkGpuInstall for performing GPU code generation environment checks

Topics

Kernels from Element-Wise Loops

Create kernels from MATLAB functions containing scalarized, element-wise math operations.

Kernels from Scatter-Gather Type Operations

Create kernels from MATLAB functions containing reduction operations.

Kernels from Library Calls

Target GPU optimized math libraries such as cuBLAS, cuSOLVER, cuFFT, and Thrust.

Support for GPU Arrays

Generate CUDA code that uses GPU arrays.

Legacy Code Integration

Integrate custom GPU code with MATLAB code intended for code generation.

Design Patterns

Create kernels for MATLAB functions containing computational design patterns.

GPU Memory Allocation and Minimization

Memory allocation options and optimizations for GPU Coder.

Featured Examples

Simulate Diffraction Patterns Using CUDA FFT Libraries

Simulate Diffraction Patterns Using CUDA FFT Libraries

Use GPU Coder™ to leverage the CUDA® Fast Fourier Transform library (cuFFT) to compute two-dimensional FFT on a NVIDIA® GPU. The two-dimensional Fourier transform is used in optics to calculate far-field diffraction patterns. When a monochromatic light source passes through a small aperture, such as in Young's double-slit experiment, you can observe these diffraction patterns. This example also shows you how to use GPU pointers as inputs to an entry-point function when generating CUDA MEX, source code, static libraries, dynamic libraries, and executables. By using this functionality, the performance of the generated code is improved by minimizing the number of cudaMemcpy calls in the generated code.

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QR Decomposition on NVIDIA GPU Using cuSOLVER Libraries

QR Decomposition on NVIDIA GPU Using cuSOLVER Libraries

Create a standalone CUDA® executable that leverages the CUDA Solver library (cuSOLVER). The example uses a curve fitting application that mimics automatic lane tracking on a road to illustrate: Fitting an arbitrary-order polynomial to noisy data by using matrix QR factorization.

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Stencil Processing on GPU

Stencil Processing on GPU

Generate CUDA® kernels for stencil type operations by implementing "Game of Life" by John H. Conway.

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Benchmark A\b by Using GPU Coder

Benchmark A\b by Using GPU Coder

Benchmark solving a linear system by generating GPU code. Use matrix left division, also known as mldivide or the backslash operator (\), to solve the system of linear equations A*x = b for x (that is, compute x = A\b).

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Fog Rectification

Fog Rectification

The use of image processing functions for GPU code generation. The example takes a foggy image as input and produces a defogged image. This example is a typical implementation of fog rectification algorithm. The example uses conv2, rgb2gray, and imhist functions.

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Stereo Disparity

Stereo Disparity

Generate a CUDA® MEX function from a MATLAB® function that computes the stereo disparity of two images.

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GPU Coder Documentation

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